3D Surface Reconstruction from Scattered Data Using Moving Least Square Method

This paper presents an efficient implementation of moving least square(MLS) approximation for 3D surface reconstruction. The smoothness of the MLS is mainly determined by the weight function where its support greatly affects the accuracy as well as the computational time in the mixed dense and scattered data. In a point-set, possibly acquired from a 3D scanning device, it is important to determine the support of the weight function adaptively depending on the distribution and shape of the given scatter data. Particulary in case of face data including the very smooth parts, detail parts and some missing parts of hair due to low reflectance, preserving some details while filling the missing parts smoothly is needed. Therefore we present a fast algorithm to estimate the support parameter adaptively by a raster scan method from the quantized integer array of the given data. Some experimental results show that it guarantees the high accuracy and works to fill the missing parts very well.